# ocr-recognize-text

> Detect and extract text fragments and their positions from an image using local OCR.

## Overview

- **Endpoint**: `POST https://api.muapi.ai/api/v1/ocr-recognize-text`
- **Model ID**: `ocr-recognize-text`
- **Category**: other
- **Variant**: OCR Recognize Text
- **Family**: ocr
- **Cost**: 0.02 credits per call (some models compute cost dynamically based on params)

## API Usage

MuApi uses a **submit-then-poll** pattern: submit a job, get a `request_id`, then poll the predictions endpoint until `status` is `completed`. Optionally pass `?webhook=YOUR_URL` on the submit call to receive a POST callback when the job finishes (skip polling).

**Authentication**: send your MuApi key in the `x-api-key` header. Get one at https://muapi.ai/access-keys.

### 1. Submit a job

```http
POST https://api.muapi.ai/api/v1/ocr-recognize-text
Content-Type: application/json
x-api-key: YOUR_API_KEY
```

**Minimum (required only):**

```json
{
  "image_url": "https://example.com/your-image_url"
}
```

**Response:**

```json
{
  "request_id": "abc123",
  "status": "processing"
}
```

### 2. Poll for the result

```http
GET https://api.muapi.ai/api/v1/predictions/{request_id}/result
x-api-key: YOUR_API_KEY
```

Possible `status` values: `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. Poll every 2-5 seconds until terminal. When `completed`, the result URLs are in the `outputs` array.

**Example response when `completed`:**

```json
{
  "id": "abc123",
  "status": "completed",
  "outputs": [
    "https://cdn.muapi.ai/.../output.png"
  ],
  "urls": {
    "get": "https://api.muapi.ai/api/v1/predictions/abc123/result"
  },
  "created_at": "2026-05-08T12:34:56Z",
  "has_nsfw_contents": []
}
```

### cURL

```bash
# 1. Submit
REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/ocr-recognize-text \
  -H "x-api-key: $MUAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"image_url":"https://example.com/your-image_url"}' | jq -r .request_id)

# 2. Poll until completed
while :; do
  RESP=$(curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY")
  STATUS=$(echo "$RESP" | jq -r .status)
  [ "$STATUS" = "completed" ] && echo "$RESP" | jq .outputs && break
  [ "$STATUS" = "failed" ] && echo "$RESP" && exit 1
  sleep 3
done
```

### Python

```python
import os, time, requests

API = "https://api.muapi.ai/api/v1"
headers = {"x-api-key": os.environ["MUAPI_API_KEY"]}

r = requests.post(f"{API}/ocr-recognize-text", headers=headers, json={"image_url":"https://example.com/your-image_url"})
request_id = r.json()["request_id"]

while True:
    res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json()
    if res["status"] == "completed":
        print(res["outputs"]); break
    if res["status"] == "failed":
        raise RuntimeError(res.get("error")); 
    time.sleep(3)
```

## Input Schema

The API accepts the following input parameters:

- **`image_url`** (`string`, _required_):
  The URL of the image to recognize text from.

## Output Schema

The polling endpoint returns the following fields:

- **`id`** (`string`): The request ID.
- **`status`** (`string`): One of `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`.
- **`outputs`** (`array`): URLs to generated images/videos/audio. Empty until `status` is `completed`.
- **`urls.get`** (`string`): Self-link to re-fetch this prediction.
- **`error`** (`string` | `null`): Error message if `status` is `failed`.
- **`created_at`** (`string`): ISO-8601 timestamp of when the request was created.
- **`has_nsfw_contents`** (`array of boolean`): Per-output NSFW detection flags.

## Webhooks (optional)

Append `?webhook=https://your-server/path` to the submit URL. When the job reaches a terminal state, MuApi will POST the same shape as the polling response to your URL — no polling needed.

## Agent Integration

MuApi ships an MCP server and CLI so agents (Claude Code, Cursor, custom) can call this endpoint without writing HTTP code:

```bash
# Install the CLI
npm install -g muapi-cli

# Authenticate once
muapi auth login

# Expose all MuApi models as MCP tools to your agent
muapi mcp serve
```

The MCP server exposes tools that wrap submit + poll for every model, including `ocr-recognize-text`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.).

## Related Models
- [Audio Profile](https://muapi.ai/playground/gemini-omni-audio)
- [Image Content Moderator](https://muapi.ai/playground/moderate-image)
- [YouTube Publish](https://muapi.ai/playground/youtube-publish)
- [Whisper](https://muapi.ai/playground/openai-whisper)
- [Instagram Publish](https://muapi.ai/playground/instagram-publish)
- [TikTok Publish](https://muapi.ai/playground/tiktok-publish)
- [Facebook Publish](https://muapi.ai/playground/facebook-publish)
- [LinkedIn Publish](https://muapi.ai/playground/linkedin-publish)
- [Twitter / X Posts Scraper](https://muapi.ai/playground/twitter-fetch-posts)
- [TikTok Profile Scraper](https://muapi.ai/playground/tiktok-fetch-profile)
- [TikTok Videos Scraper](https://muapi.ai/playground/tiktok-fetch-videos)
- [Instagram Reels Scraper](https://muapi.ai/playground/instagram-fetch-reels)

## Resources
- [Playground Page](https://muapi.ai/playground/ocr-recognize-text)
- [API Reference](https://muapi.ai/playground/ocr-recognize-text?tab=2)
- [Global llms.txt](https://muapi.ai/llms.txt)